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Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter
Author(s) -
Grantham Neal S.,
Reich Brian J.,
Liu Yang,
Chang Howard H.
Publication year - 2018
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2499
Subject(s) - covariate , missing data , statistics , particulates , environmental science , econometrics , regression analysis , spatial analysis , regression , computer science , mathematics , ecology , biology
The United States Environmental Protection Agency has established a large network of stations to monitor fine particulate matter of <2.5 µm (PM 2.5 ) that is known to be harmful to human health. Unfortunately, the network has limited spatial coverage, and stations often only measure PM 2.5 every few days. Satellite‐measured aerosol optical depth (AOD) is a low‐cost surrogate with greater spatiotemporal coverage, and spatial regression models have established that including AOD as a covariate improves the spatial interpolation of PM 2.5 . However, AOD is often missing, and our analysis reveals that the conditions that lead to missing AOD are also conducive to high AOD. Therefore, naïve interpolation that ignores informative missingness may lead to bias. We propose a joint hierarchical model for PM 2.5 and AOD that accounts for informatively missing AOD. We conduct a simulation study of the effects of ignoring informative missingness in the covariate and evaluate the performance of the proposed model. We apply the method to map daily PM 2.5 in the Southeastern United States. Our analysis reveals statistically significant informative missingness and relationships between PM 2.5 and AOD in many seasons after accounting for meteorological and land‐use variables.